On the Evaluation of the Most Accurate Pediatric Medulloblastoma Animal Model

• Activity Number: 193
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Behrouz Shamsaei* and Cuilan Gao
• Companies: and University of Tennessee Chattanooga
• Keywords: human disease ; Medulloblastoma ; Animal Model ; gene expression data ; ANOVA

Abstract

Animal models of human disease are commonly utilized to gain preclinical insight into the potential efficacy and action mode of novel drugs. The development and selection of an animal model that accurately mimics the human disease profoundly reduces the research timeline and resources needed to make meaningful advances in the treatment and prevention of the human disease under study. Here, we propose a statistical procedure to select the animal model that most accurately mimics the human disease in terms of genome-wide gene expression. Our procedure is designed for studies that have gene expression profiles for a cohort of human disease tissue specimens from different subjects and gene expression profiles for cohorts of disease tissue specimens for each of several animal models. First, we define and compute a metric of similarity between each human gene expression profile and animal gene expression profile which result in multiple groups of similarities. Then a random block ANOVA model is used to compare the group means of similarities between different animal models. Finally post-hot multiple comparison is applied to seek the ``best' animal model of the human disease. The advantages of the proposed method are observed in simulation studies and a real example of pediatric Medulloblastoma.